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Predicting Mobility Using Limited Data During Early Stages Of A Pandemic, Michael T. Lash, S. Sajeesh, Özgür M. Araz
Predicting Mobility Using Limited Data During Early Stages Of A Pandemic, Michael T. Lash, S. Sajeesh, Özgür M. Araz
Department of Marketing: Faculty Publications
The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize …